基于机器学习的工业机器人关节故障诊断 -机械设计制造及其自动化专业

摘要

随着工业机器人应用范围的不断扩大，其关节故障诊断的准确性和效率成为保障生产安全和提高设备利用率的关键问题。传统的故障诊断方法依赖于专家经验和复杂的物理模型，难以应对复杂多变的工业环境。本文提出了一种基于机器学习的工业机器人关节故障诊断方法，旨在通过数据驱动的方式提升诊断的智能化水平。研究首先对工业机器人关节的运行数据进行采集与预处理，构建了包含多种故障模式的数据集。随后，采用深度学习中的卷积神经网络和长短期记忆网络相结合的方法，构建了一个多层次的故障诊断模型。该模型能够有效捕捉关节运行中的时序特征和空间特征，显著提高了故障识别的准确率。实验结果表明，相较于传统方法，本文提出的模型在故障检测率和误报率上均有显著提升，尤其在复杂工况下表现尤为突出。

关键词：工业机器人；故障诊断；机器学习；卷积神经网络

JOINT FAULT DIAGNOSIS OF INDUSTRIAL ROBOT BASED ON MACHINE LEARNING

ABSTRACT

With the continuous expansion of the application range of industrial robots, the accuracy and efficiency of joint fault diagnosis have become the key issues to ensure production safety and improve the utilization rate of equipment. Traditional fault diagnosis methods rely on expert experience and complex physical model, which is difficult to deal with complex and changeable industrial environment. This paper proposes a machine learning-based joint fault diagnosis method for industrial robots, aiming to improve the intelligent level of diagnosis through a data-driven approach. Firstly, the operation data of industrial robot joints are collected and preprocessed, and a data set containing multiple fault modes is constructed. Then, a multi-level fault diagnosis model is constructed by combining convolutional neural network and long and short term memory network in deep learning. This model can effectively capture the temporal and spatial characteristics of joint operation, and significantly improve the accuracy of fault identification. Experimental results show that compared with traditional methods, the proposed model has significantly improved the fault detection rate and false positive rate, especially in complex conditions.

KEY WORDS：Industrial Robot; Fault Diagnosis; Machine Learning; Convolutional Neural Network

摘要 I

ABSTRACT II

第1章绪论 2

1.1 研究背景及意义 2

1.2 基于机器学习的工业机器人关节故障诊断研究现状 2

第2章工业机器人关节故障特征提取与分类方法 3

2.1 关节故障特征提取的机器学习模型 3

2.2 基于深度学习的故障特征分类算法 3

2.3 特征提取与分类方法的实验验证与分析 4

第3章基于机器学习的工业机器人关节故障预测模型 5

3.1 关节故障预测模型的构建与优化 5